Abstract
Several experiments have demonstrated that spontaneous brain activity is not random. Atthe level of large-scale neural systems, the ongoing activity measured with functional MRI(fMRI) reflects the organization of a series of highly coherent functional networks [1]. Al-though methodologies based on fMRI are highly reliable in spatial resolution, they lack timeresolution, which is indeed the strength of EEG-based methodologies. However, using EEGfor studying functional connectivity is severely limited by volume conduction and its accuracystrongly depends on source modelling. To avoid these problems we propose an approachbased on intra-cerebral EEG recordings (stereo-EEG - SEEG) in humans. In the presentwork we have applied a graph theoretical approach to analyze a set of SEEG signals, ac-quired during a resting-state condition. With the developed tools we are able to investigatethe salient characteristics of the brain regions involved and to find preferred pathways andconnection hubs.
Highlights
Several experimental works have demonstrated that, apparently, spontaneous brain activity is not random
Mainly interested on measuring the functional distances to be able to compare them with geometrical distances, we computed a specific metric according the normalized mutual information matrix
Six patients with drug resistant epilepsy had been implanted with multi-channels depth electrodes for clinical evaluation [2]
Summary
Several experimental works have demonstrated that, apparently, spontaneous brain activity is not random. Mainly interested on measuring the functional distances to be able to compare them with geometrical distances, we computed a specific metric according the normalized mutual information matrix. Statistical analysis has been employed using Rank Order test, with p
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